Conference paper Open Access

A Weighted Late Fusion Framework for Recognizing Human Activity from Wearable Sensors

Athina Tsanousa; Georgios Meditskos; Stefanos Vrochidis; Ioannis Kompatsiaris


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        <foaf:name>Athina Tsanousa</foaf:name>
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        <foaf:name>Ioannis Kompatsiaris</foaf:name>
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    <dct:title>A Weighted Late Fusion Framework for Recognizing Human Activity from Wearable Sensors</dct:title>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2019</dct:issued>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2019-07-15</dct:issued>
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    <dct:description>&lt;p&gt;Following the technological advancement and the&lt;br&gt; constantly emerging assisted living applications, sensor-based activity&lt;br&gt; recognition research receives great attention. Until recently,&lt;br&gt; the majority of relevant research involved extracting knowledge&lt;br&gt; out of single modalities, however, when individual sensors performances&lt;br&gt; are not satisfactory, combining information from multiple&lt;br&gt; sensors can be of use and improve the activity recognition rate.&lt;br&gt; Early and late fusion classifier strategies are usually employed&lt;br&gt; to successfully merge multiple sensors. This paper proposes a&lt;br&gt; novel framework for combining accelerometers and gyroscopes&lt;br&gt; at decision level, in order to recognize human activity. More&lt;br&gt; specifically, we propose a weighted late fusion framework that&lt;br&gt; utilizes the detection rate of a classifier. Furthermore, we propose&lt;br&gt; the modification of an already existing class-based weighted late&lt;br&gt; fusion framework. Experimental results on a publicly available&lt;br&gt; and widely used dataset demonstrated that the combination of&lt;br&gt; accelerometer and gyroscope under the proposed frameworks&lt;br&gt; improves the classification performance.&lt;/p&gt;</dct:description>
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